Various machines used in working environments in a field may perform a variety of operations or tasks. Knowing how such machines are being operated in the field (e.g., a work site) may give valuable insight into machine events and user usage patterns. Machine operations may include, but are not limited to including, tasks such as dig, dump, travel, idle, push, rip, heavy blade, light blade, ditch, cut, and the like. The machine operations may be based on what type of machine is being observed. Such machine types include, but are not limited to including, a motor grader, a track type tractor, a bulldozer, a paver, an electric rope shovel, and any other machine performing tasks at a worksite.
Analytical models may be developed to predict the operations of machines and related tasks based on input data from the machine based on on-board engineering channels. The input data may include conditions taken from system sensors or other data collection devices associated with the machine. Input data may include, but is not limited to including, machine torque, machine gears and gear ratios, readings from hydraulic sensors associated with lifts, ground and/or track speeds, slope data, and any other data indicative of a machine operation or task that is received from a sensor or device associated with the machine. Further, said input data may be used to derive data, based on physics, to determine data associated with an operation or task. Systems and methods for predicting operations using sensors have been employed, like, for example the systems disclosed in U.S. Pat. No. 4,035,621 (“Excavator Data Logging”), which uses sensor data to determine operation of an excavator.
Certain sets of data from the input data from the on-board engineering channels may be indicative of machine operations and/or tasks. Thusly, groups of data may be arranged in ways in which an analytics system may “predict” the operation of the machine based on data from the on-board engineering channels. However, such predictions may need to be assisted by user input for establishing rules or other manual methods for determining rules to predict an operation. Using such manual methods may be burdensome to the user and computationally inefficient.
A method for using on-board engineering channel data to more accurately determine machine operations is desired. Therefore, systems and methods for developing machine operation classifiers using machine learning are desired for creating predictive models for machine operations with greater accuracy and computational efficiency.